Passive Localization Research Articles

Time Difference of Arrival Estimation of Stationary Targets Utilizing Deep Neural Network

Time difference of arrival (TDOA) is widely used in the field of passive location because of its flexible base station deployment and simple principle. However, when solving the solution of stationary targets position, the traditional method, such as the two-stage weighted least squares (TSWLS) algorithm, is easily affected by noise, and cannot achieve good localization results in practical situations. To solve this problem, we propose a deep neural network (DNN) for TDOA estimation of stationary targets. First, a large number of simulation samples are generated according to the TDOA model. Each sample contains the time difference from each secondary base station to the main base station, the error of time difference, and the real three-dimensional (3-D) coordinates of the targets. Next, a suitable DNN architecture is designed to solve the solution of the stationary target position. The simulation results prove that the method proposed herein outperforms TSWLS for multiple base stations based on the TDOA model.

Study on passive location method of shallow water acoustic source with single hydrophone

In order to meet the needs of target detection in shallow water with concealed and low energy consumption platform, the Bayesian estimation theory was applied to underwater acoustic location. The underwater acoustic location model was established based on probability density function as the description of sound source state, so as to overcome the problems of unstable shallow water environment and mismatching of sound field model. Histogram filtering method is used to solve the integral solution in the process of posterior probability estimation of sound source state. The hierarchical grid histogram filtering method is proposed for the first time, which effectively improves the efficiency of histogram filtering iterative algorithm. The measured data and simulation results of SWelleX-96 show that the depth positioning accuracy can reach 35 m and the distance positioning accuracy can reach 0.69 km within the range of 200 m deep and 10 km long in shallow water environment, and the efficiency of the algorithm can be improved by N1/2 times.

Underwater target passive acoustic localization method based on Hanbury Brown–Twiss interference

PurposeAcoustic signals of the underwater targets are susceptible to noise, reverberation, submarine topography and biology, therefore it is difficult to precisely locate underwater targets. This paper proposes a new underwater Hanbury Brown-Twiss (HBT) interference passive localization method. This study aims to achieve precise location of the underwater acoustic targets.Design/methodology/approachThe principle of HBT interference with ultrasensitive detection characteristics in optical measurements was introduced in the field of hydroacoustics. The coherence of the underwater target signal was analyzed using the HBT interference measurement principle, and the corresponding relationship between the signal coherence and target position was obtained. Consequently, an HBT interference localization model was established, and its validity was verified through simulations and experiments.FindingsThe effects of different array structures on the localization performance were obtained by simulation analysis, and the simulations confirmed that the HBT method exhibited a higher positioning accuracy than conventional beamforming. In addition, the experimental analysis demonstrated the excellent positioning performance of the HBT method, which verified the feasibility of the proposed method.Originality/valueThis study provides a new method for the passive localization of underwater targets, which may be widely used in the field of oceanic explorations.

Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder

Passive localization of underwater targets was a thorny problem in underwater acoustics. For traditional model-driven passive localization methods, the main challenges are the inevitable environmental mismatch and the presence of interference and noise everywhere. In recent years, data-driven machine learning approaches have opened up new possibilities for passive localization of underwater acoustics. However, the acquisition and processing of underwater acoustics data are more restricted than other scenarios, and the lack of data is one of the most enormous difficulties in the application of machine learning to underwater acoustics. To take full advantage of the relatively easy accessed unlabeled data, this paper proposes a framework for underwater acoustic source localization based on a two-step semi-supervised learning classification model. The first step is trained in unsupervised mode with the whole available dataset (labeled and unlabeled dataset), and it consists of a convolutional autoencoder (CAE) for feature extraction and self-attention (RA) mechanism for picking more useful features by applying constraints on the CAE. The second step is trained in supervised mode with the labeled dataset, and it consists of a multilayer perceptron connected to an encoder from the first step and is used to perform the source location task. The proposed framework is validated on uniform vertical line array data of SWellEx-96 event S5. Compared with the supervised model and the model without the RA, the proposed framework maintains good localization performance with the reduced labeled dataset, and the proposed framework is more robust when the training dataset and the test dataset of the second step are distributed differently, which is called “data mismatch.”

Rocket Recovery Carbin Section Passive location based on whale optimization algorithm

The accurate location of the rocket recovery cabin section is one of the key technologies to realize the reliable recovery rockets. Based on the multi-station time difference of arrival (TDOA) passive location principle, a technical solution for the recovery and positioning of the rocket recovery cabin section is designed. Through the active radiation signal of the recovery cabin, the whale optimization algorithm (WOA) is used to locate the position of the rocket recovery cabin section. The recovery process trajectory of the rocket's stage 1 cabin was adopted to verify the feasibility of the designed recovery positioning technical scheme.

Enhancing cross correlations of ocean ambient noise in the time domain based on random matrix theory.

Cross correlations of diffuse noise can be utilized to recover the time domain Green's function (TDGF) between two points. This principle allows for a variety of practical applications, such as seismic tomography, ocean thermometry, passive localization, etc. However, in the real ocean environment, loud interference noise sources usually bias the travel time estimates of the TDGF or result in poor recovery quality. To deal with this issue, a diffuse noise reconstruction approach is proposed to eliminate the influence of the strong interference noise by utilizing the time domain statistical property of ocean ambient noise recorded on single hydrophones with the help of random matrix theory. Simulation and experimental data analysis indicate that this algorithm can effectively extract the diffuse noise component from the ocean ambient noise field and retrieve the TDGF with a higher signal-to-noise ratio when coherent accumulation of cross correlations of the reconstructed diffuse noise is performed.

Passive localisation of mixed near‐field and far‐field signals using the transformed symmetric nested array

Passive localisation of mixed near‐field and far‐field signals using the transformed symmetric nested array

3-D Indoor Localization and Identification Through RSSI-Based Angle of Arrival Estimation With Real Wi-Fi Signals

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.

Efficiency of the Position Tracking Photovoltaics using Microcontroller Atmega

So far, solar cells are known to have many weaknesses in terms of installation and position to the sun, so that the performance of solar cells on rechargeable batteries is less than optimal. Therefore, additional tools are needed to support these solar cells so that they can work optimally and produce bigger currents. Compare a sunlight tracker with an LDR system, and compare a sunlight tracker with a direct sunlight sensor with a passive system. Atmega16 is used as a control motor and automatic charger, LDR is used as a sunshine receiver, and a DC motor and a stepper are used as a sunshine indicator player. The solar cell used has a capacity of 20 WP. The automatic filling system uses a microcontroller to increase the cost and efficiency of installation. After that, the two systems will be compared first to find out the difference in power generated by the solar cell system using a tracer and a passive tracer. Using a tracking system will collect data for a week, while using a passive system will collect data for a week. By using the result data to be analyzed, the system can be analyzed and compared, and its efficiency can be determined. The solar cell tracking system is expected to increase battery charge faster than passive locations

Research on monostation passive location technology based on infrared tracking system

The ability to track and locate aerial targets is of great significance to target monitoring and recognition, as well as other applications. The traditional infrared tracking system needs the assistance of active ranging methods, such as laser and radar, or binocular vision and multistation detection methods for positioning. Based on high-resolution infrared tracking systems, which measure the target temperature and angle, and the law that target temperature changes with height, a monostation passive target location method based on infrared quantitative detection is proposed. The theoretical model of target location, which comprehensively considers the factors of temperature retrieval, aerodynamic heating, and atmosphere, is established. The location experiment is conducted with the developed high-resolution infrared tracking system. Compared with the automatic dependent surveillance-broadcast data of the actual target, the relative error of the ranging is <5 % . The error sources of this method are simulated and analyzed, and the simulation of the location distance and the error analysis of the long-distance target are conducted. Results show that the proposed method effectively locates the target. The method improves the monostation location ability of the infrared system and has a certain application value.

Use of Thermal Radiation for Remote Identification of Liquids in Closed Dielectric Containers

Remote determination of substances, including liquids, is an urgent problem, the solution of which requires fundamentally new approaches to both methods and measurement techniques. This is due to the fact that standard liquid analysis methods are mainly spectroscopic methods in various frequency ranges from neutron diffractometry to acoustic spectroscopy, which are very difficult or even impossible to apply in remote sensing. Conventional radar methods are usually unacceptable due to the impossibility of covert removal of information using active location and obtaining data on the composition of the irradiated object based on it. Therefore, at present, methods and techniques of passive location are being used more and more, but in order to solve quite specific problems of remote identification of substances, they must be significantly modernized.&#x0D; This article discusses a technique and a modernized measuring complex for remote study of liquid electrolytes and non-electrolytes using measurements and subsequent analysis of fluctuations in the temperature difference of the intermediate liquid medium (distilled water) in which the container with the liquid under study is located. To date, there is no clear understanding of how thermally stimulated radiation depends on the composition of a macroscopic body, and also on whether thermal radiation is in equilibrium or non-equilibrium. The investigated object, at a given temperature, emits electromagnetic waves associated with the thermal motion of the molecules and atoms of its constituents. That’s, why thermal radiation is random (fluctuation) process, which means that the average value of the electromagnetic field is equal to zero, but other averaged features, such as quadraticity in the field, may differ from zero. The quadratic characteristics of a thermal electromagnetic field determine its energy, which has a finite value other than zero, which can be measured by an appropriate sensor and characterizes the field source — a macroscopic body heated to a certain temperature. The paper presents the results of studies of mixtures of electrolytes and non-electrolytes of various concentrations. An example of non-equilibrium thermal radiation is given. It is shown that it is possible to distinguish between the studied liquids and methods of combining the components of solutions of given concentrations to obtain the given properties of the entire solution.

Frequency-Diversity-Based Underwater Acoustic Passive Localization

This article considers underwater acoustic passive localization of a noncooperative broadband source in the presence of multipath propagation. A differential transmission-loss model-based passive localization method is proposed, in which 3-D localization can be implemented with only two hydrophones. Instead of spatial diversity, frequency diversity is exploited to achieve passive localization with a limited number of hydrophones and address the challenge of multipath propagation. Specifically, the received signals of both hydrophones are decomposed in the frequency domain, respectively, at first. Then, a set of differential transmission-loss model-based equations between the two hydrophones are established on multiple frequencies. Based on these equations, the passive localization is modeled as a multivariate optimization problem. Meanwhile, the nonline of sight (NLOS)-related parameters are also involved in the optimization problem. Therefore, accurate localization and NLOS mitigation can be accomplished simultaneously by solving the optimization problem. To simplify the multivariate optimization problem for a reliable solution, a cepstrum-autocorrelation-based multipath estimation algorithm is proposed, by which all the NLOS paths can be represented by an equivalent NLOS path. Consequently, the simplified optimization problem concerns only the desired coordinate of the source and a single NLOS-related parameter. Finally, a differential evolution algorithm is employed to solve the simplified optimization problem. Both simulation and lake trial results corroborate the effectiveness and the robustness of the proposed method.

Indoor Localization for Intrusion Detection and Monitoring RSSI Based

Abstract: A real time machine for Intrusion detection &amp; monitoring primarily based on wireless sensor network technology is designed by using the use of the mote that's developed &amp; designed in as the communication module inside the network. This dissertation tells tool-free Passive Localization with RSSI. Primary objective of this dissertation is to layout D.F.P Localization that can be redeployable, reconfigurable, smooth to apply, &amp; operates in actual time. The embedded intrusion detection algorithm is designed so that it may cope with the limited assets, in terms of computational energy &amp; available memory area, of the microcontroller unit (MCU) determined inside the nodes &amp; various demanding situations &amp; problem confronted during the real check mattress deployment &amp; additionally proposed solutions to conquer. We come up with an opportunity algorithm primarily work on minimum Euclidean distance classifier the output suggests that localization accuracy of the method is accelerated while usage of the proposed algorithm.

Recent Advances in Passive Acoustic Localization Methods via Aircraft and Wake Vortex Aeroacoustics

Passive acoustic aircraft and wake localization methods rely on the noise emission from aircraft and their wakes for detection, tracking, and characterization. This paper takes a holistic approach to passive acoustic methods and first presents a systematic bibliographic review of aeroacoustic noise of aircraft and drones, followed by a summary of sound generation of wing tip vortices. The propagation of the sound through the atmosphere is then summarized. Passive acoustic localization techniques utilize an array of microphones along with the known character of the aeroacoustic noise source to determine the characteristics of the aircraft or its wake. This paper summarizes the current state of knowledge of acoustic localization with an emphasis on beamforming and machine learning techniques. This review brings together the fields of aeroacoustics and acoustic-based detection the advance the passive acoustic localization techniques in aerospace.

Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification.

Ice environments pose challenges for conventional underwater acoustic localization techniques due to their multipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are a strong contender for tracking moving acoustic sources on ice. Additionally, we show that classification as a localization technique can outperform regression for networks more suited for classification, such as the CNN and ViTs.

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Accurate Delay Estimation for Multisensor Passive Locating Systems Exploiting the Cross-Correlation Between Signals Cross-Correlations

The availability of accurate estimates of the delay or time of arrival of the incoming signals is of paramount importance for the position estimation in passive radars with multiple receivers. This correspondence aims at improving estimation of the delays by multiple detectors exploiting the cross-correlation between the cross-correlation estimates (say cross-cross-correlation) of the received signals. The resulting equation system is formulated as a least squares minimization problem, whose solution is efficiently found computing the pseudoinverse of the model matrix. In fact, the cross-cross-correlation implicitly performs a filtering operation on the considered signal, approximating the generalized cross-correlator behavior, without using statistical information about the signal spectra. The proposed method is numerically validated in comparison with classic counterparts and theoretical bounds.

A Multi-Target Passive Location Method Based on GDOP Value and Beam Resolution

In passive location systems on the ground, the judgment and location of multi-target is more challenging compared with the case of single target. In this paper, we propose a method for multi-target identification and location in an arbitrary structure with three base stations (BSs). First of all, we discuss the scene of multi-targets judgment based on geometric dilution of precision (GDOP) value. Secondly, we propose an algorithm that calculates the system coverage radius based on arbitrary three BS structures. The algorithm helps to identify the number of targets for unsupervised learning. Finally, we locate each target individually located again based on the linear constrained minimum variance (LCMV) beam former and time difference of arrival (TDOA) algorithm. In the simulations, we analyzed the location dispersion under different signal-to-noise ratio (SNR), then calculated the termination threshold of the k-means algorithm under different SNR. The simulation results show that, compared to the probability hypothesis density (PHD) filter and TDOA-angle-of-arrival (AOA) joint algorithm, the proposed method can increase more than 12.5% and 15.6% points. With the increase of the number of targets, the running time of our algorithm is controllable with better stability.

Research on passive localization method of shallow water acoustic source with single hydrophone based on hierarchical grid histogram filtering

An acoustic field model in the form of a probability density function is established to solve the problem of environmental instability in shallow water. A Bayesian underwater acoustic localization model with the state vector of the acoustic source as a posteriori probability is generated for the purpose of trading time for space in an iterative form and realizing localization of a moving source with a single hydrophone. A hierarchical grid histogram filtering algorithm is proposed, which solves the integral problem in the process of Bayesian filtering and improves the efficiency of the histogram filtering algorithm greatly. The SWellEx-96 experimental results show that in the range of 200 m * 10 km, the relative error of depth positioning is controlled at 12%, the relative error of distance positioning is controlled at 7%, and the efficiency of the algorithm is improved by N1/2 times where N is the number of grids of the histogram filter. The method proposed in this paper is suitable for concealed and low energy consumption platforms in shallow water.

Real-Time &amp; Passive Localization of the Epileptic Network based on Single-Pulse Electrical Stimulation: Correlation with the standard of care and surgical outcomes UPMC HBM Lab (S7.004)

Real-Time &amp; Passive Localization of the Epileptic Network based on Single-Pulse Electrical Stimulation: Correlation with the standard of care and surgical outcomes UPMC HBM Lab (S7.004)